出错 Untitled (第 2 行) num_range = [1:800; 1600:2400; 3200:4200; 4800:5600; 6500:7400; 8134:8959; 9768:10610; 11380:12210; 13020:13840; 14650:15480];

这个错误可能是由于您的Matlab版本不支持在单个语句中使用分号分隔的行向量来初始化二维数组导致的。可以将每个行向量分开并分配给num_range的每一行，例如：

range1 = 1:800;
range2 = 1600:2400;
range3 = 3200:4200;
range4 = 4800:5600;
range5 = 6500:7400;
range6 = 8134:8959;
range7 = 9768:10610;
range8 = 11380:12210;
range9 = 13020:13840;
range10 = 14650:15480;

num_range = [range1; range2; range3; range4; range5; range6; range7; range8; range9; range10];

这样应该可以解决这个问题。

相关问题

function untitled() load('D:\mat格式的MNIST数据\test_labels.mat') load('D:\mat格式的MNIST数据\train_images.mat') load('D:\mat格式的MNIST数据\train_labels.mat') load('D:\mat格式的MNIST数据\test_images.mat') train_num = 6000; test_num = 200; %训练数据,图像转向量 data_train = mat2vector(train_images(:,:,1:train_num),train_num); data_test = mat2vector(test_images(:,:,1:test_num),test_num); % 处理训练数据,防止后验概率为0 [data_train,position] = fun(data_train,train_labels1(1:train_num)'); % 处理测试数据 for rows = 1:10 data_test(:,position{1,rows})=[]; end %模型部分 % 超参数全部取了默认值，比较重要的，如类别的先验概率，如果不进行修改，则计算输入数据中类别的频率 % 查看nb_model即可确认所使用的超参数 Mdl = fitcnb(data_train,train_labels1(1:train_num)); %训练模型 %测试结果 result = predict(Mdl,data_test); result = result.'; xlabel=[0,1,2,3,4,5,6,7,8,9]; resultbar = [0,0,0,0,0,0,0,0,0,0]; testbar = [0,0,0,0,0,0,0,0,0,0]; for i = 1:test_num temp1=result(i); temp1=temp1+1; resultbar(temp1)=resultbar(temp1)+1; temp2=test_labels1(i); temp2=temp2+1; testbar(temp2)=testbar(temp2)+1; end bar(xlabel, [resultbar' testbar']); % 整体正确率 acc = 0.; for i = 1:test_num if result(i)==test_labels1(i) acc = acc+1; end end title('精确度为：',(acc/test_num)*100) end function [output,position] = fun(data,label) position = cell(1,10); %创建cell存储每类中删除的列标 for i = 0:9 temp = []; pos = []; for rows = 1:size(data,1) if label(rows)==i temp = [temp;data(rows,:)]; end end for cols = 1:size(temp,2) var_data = var(temp(:,cols)); if var_data==0 pos = [pos,cols]; end end position{i+1} = pos; data(:,pos)=[]; end output = data; end function [data_]= mat2vector(data,num) [row,col,~] = size(data); data_ = zeros(num,row*col); for page = 1:num for rows = 1:row for cols = 1:col data_(page,((rows-1)*col+cols)) = im2double(data(rows,cols,page)); end end end end你能告诉我这个代码里关于画图的吗

这个代码里使用了MATLAB自带的`bar`函数来画柱状图。`bar(xlabel, [resultbar' testbar'])`会在x轴上画出0到9的标签，然后在y轴上分别画出`resultbar`和`testbar`的值。其中，`resultbar`是预测结果中0到9的数量，`testbar`是测试数据中0到9的数量。`title('精确度为：',(acc/test_num)*100)`用来在图像上显示测试集的准确率。